The goal of this project is to develop integrative approaches for the detection of biomarkers from multiscale genomic and imaging data, so that multiple mental illnesses such as schizophrenia (SC), Unipolar (UD) and bipolar (BI) disorder can be better identified. Imaging genetics is an emerging technique, which integrates imaging and genomic approaches to explore the association between genetic variations and brain functions and behaviors. Although it promises a better and more powerful approach for disease diagnosis and prognosis, the field is facing several major challenges: 1) First, most of current imaging genetics studies focus on pair-wise data correlation and integration; other important genetic factors such as epigenomics and genetic interactions (epistasis) have not been incorporated. 2) Second, multiscale imaging genetics data often exhibit specific characteristics such as inter- correlations, but this prior knowledge has not been incorporated into existing integrative models. 3) Finally, there is a high dimensionality problem with the analysis of imaging genetic data the number of sample is always significantly less than that of features. The solution of these problems necessitates a paradigm shift in computational models by considering the specific characteristics of these multiscale and multimodal data. Our multidisciplinary research team consisting of imaging scientist (Dr. Calhoun), statistical geneticist (Dr. Deng), biomedical engineer and bioimaging informatician (Dr. Wang), and psychiatrist (Dr. Pearson) has worked productively and creatively over the past few years in developing a number of data integration methods for fusion of imaging and genomic data. Building on our initial success, we will accomplish the following specific aims: 1) to study the correlation between multiple imaging and genomic data for the detection of epistasis factors or interaction networks; 2) to integrate multiscale imaging and genomic data, especially incorporating epistasis factors, for the identification of biomarkers, from which risk genes can be better detected; 3) to apply the detected biomarkers for the classification of multiple mental illnesses that are currently based on symptoms and are often misdiagnosed; and 4) to develop and disseminate an open source sparse model based data integration toolbox to the broad research community. The project will make significant impact on more accurate classification of clinically cryptic subgroups (e.g., SC, UD, BI) with an innovative and integrative paradigm by taking into account specific features of multiscale imaging genomic data and incorporation of prior knowledge. This will bring transformative changes on the current diagnosis of these mental illnesses (e.g., primarily based on imaging symptoms, which are often inaccurate), promising for personalized and optimal treatments. The developed methodology and tools are also applicable to many other neurological and psychiatric disorders. By the dissemination of the developed software tools to the research community, the project will have a broad and sustained impact.